PROJECT SUMMARY This R21 addresses a critical need for accurate and scalable screening tools able to detect autism spectrum disorder (ASD) within the first year of life. This project will pilot an innovative digital phenotyping screening method, which uses computer vision and machine learning to measure synchrony within simple infant-caregiver interactions. Synchrony refers to the tendency for infants to spontaneously and dynamically coordinate their behaviors with their caregivers in time. This critical and early-emerging developmental process may provide unique and precise information about an infant?s risk for ASD, while also offering a lens for understanding early social interaction differences at the core of ASD. Significance: This project represents a paradigm shift in ASD screening, moving beyond behavior rating scales toward methods that are better suited to capture the subtle early indicators of ASD. Caregiver rating scales lack the granularity and objectivity necessary for detecting signs of ASD that emerge slowly and subtly throughout the first year. Approach: The interdisciplinary study team will leverage cutting-edge technology to objectively and granularly measure synchrony within 5-minute, play-based infant-caregiver interactions. Markerless computer vision will be used to quantify facial movements, captured unobtrusively with small, bidirectional cameras. The dyadic synchrony among infants? and caregivers? facial movements will then be calculated throughout the interaction, as part of an automated machine learning pipeline. Preliminary Data: We evaluated this approach in young adults with and without ASD during brief conversational interactions with research staff members. In a machine learning analysis pipeline, synchrony features classified diagnosis with 91% accuracy - significantly better than expert clinicians assessing the same videos. The same set of synchrony features significantly predicted symptom severity in the ASD group, suggesting that this method is effective for both diagnostic classification and dimensional prediction of individual differences. Importantly, the pipeline also classified diagnosis in children with similarly high accuracy, demonstrating the reproducibility of results across age groups. Aims. This project extends these computer vision-based methods to infants, with the overarching goal of evaluating their utility as a Level II screener for ASD. Aim 1 will evaluate the concurrent validity of our computational measures of interactional synchrony by evaluating their relationships with an established clinician-administered assessment of early ASD markers. Aim 2 will assess the utility of our interactional synchrony measure as a Level II screening tool at 12 months, by testing its ability to predict future ASD diagnosis with high specificity. Impact: This R21 will provide initial validation for a novel, computer vision- based screener for ASD in infancy. By targeting the dynamics of natural infant-caregiver interactions, this method has the potential to identify very early signs of disrupted social development, even before classic ASD symptoms emerge. Moreover, this quick interaction-based screener would fit easily into the context of routine pediatric care, holding promise as a Level II screener deployable within a universal screening framework.